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Internship Ai Data Labeling Jobs in Texas (NOW HIRING)

What if your language expertise could help improve the speech and voice AI systems used by millions of people worldwide? WHAT YOU'LL DO * Execute Data labelling and annotation tasks across speech and ...

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Internship Ai Data Labeling information

What is the difference between Internship Ai Data Labeling vs Data Annotation Specialist?

AspectInternship Ai Data LabelingData Annotation Specialist
Required CredentialsHigh school diploma or equivalent; some technical skillsHigh school diploma or higher; technical skills often preferred
Work EnvironmentEntry-level, training-focused, often remote or in-officeProfessional setting, may be remote or on-site, more independent
Employer & Industry UsageTech companies, AI startups, research projectsAI companies, data service providers, tech firms
Search & Comparison IntentUnderstanding entry-level roles in AI data labelingClarifying professional data annotation roles

Internship Ai Data Labeling typically refers to entry-level, training-focused positions aimed at gaining experience in labeling data for AI models. Data Annotation Specialist is a more experienced, professional role involving detailed data labeling tasks. Both roles are essential in AI development, but internships are designed for beginners, while specialists have more responsibility and expertise.

What is an AI data labeling internship?

An AI Data Labeling Internship is a temporary position where interns assist in preparing datasets for machine learning models by accurately annotating, categorizing, or tagging data such as images, text, or audio. Interns learn about the fundamentals of artificial intelligence and the importance of high-quality labeled data in training algorithms. This role is ideal for students or recent graduates interested in AI, data science, or related fields, and provides hands-on experience with data preparation and quality assurance processes.

What are some common challenges faced during an AI data labeling internship, and how can I overcome them?

As an AI Data Labeling intern, you may encounter challenges such as maintaining high accuracy while labeling large volumes of data, understanding complex labeling guidelines, and managing repetitive tasks without losing focus. To overcome these, it's helpful to regularly review the instructions, seek feedback from your team lead, and use productivity techniques to stay engaged. Collaborating with other interns and attending team meetings can also provide valuable insights and help you address uncertainties quickly.

What are the key skills and qualifications needed to thrive as an AI data labeling intern?

To thrive as an AI Data Labeling Intern, you need attention to detail, basic data analysis skills, and familiarity with data annotation concepts, often supported by a background in computer science or related fields. Experience using annotation platforms, spreadsheets, and sometimes specific labeling software is common, though formal certifications are not usually required. Strong communication, time management, and the ability to follow detailed guidelines set high performers apart in this role. These skills ensure accurate, high-quality data sets that are essential for training reliable AI models.
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Infographic showing various Internship Ai Data Labeling job openings in Texas as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Data Labeling Associate

Welo Data

Austin, TX

$34/hr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 28 days ago


Job description

What if your language expertise could help improve the speech and voice AI systems used by millions of people worldwide?
WHAT YOU’LL DO
• Execute Data labelling and annotation tasks across speech and voice datasets.
• Work with audio and language data, including transcription, categorization, and tagging.
YOU ARE A FIT IF YOU’RE…
• A English with Dialect from (Australian, United Kingdom and Canadian) speaker with strong written communication skills
• Experienced in data labelling, annotation, content review, or similar detail-oriented work (2+ year preferred)
• A Bachelor's degree holder
PROJECT DETAILS
• Location: 100% Onsite (Bay Area, Seattle, NYC, or client-dependent locations)
• Employment Type: W2 Full-Time Employee
• Hours: 40 hours per week
• Work Authorization: Must be authorized to work in the U.S. (no visa sponsorship available)
• Eligible Locations: NYC, Seattle, Bellevue, Redmond, San Francisco, Sunnyvale, Burlingame, Austin, Los Angeles, Washington DC, Chicago, and Boston
BENEFITS
• $34 per hour
• Paid Vacation (6 days)
• Paid Company Holidays
• Paid Sick Leave
• Employee Assistance Program
• Health Savings Account (HSA)
• 401(k) Retirement Plan
• Additional Voluntary Benefits (Life, Accident, Critical Illness, etc.)
ADDITIONAL BENEFITS (Upon Eligibility)
• Medical, Dental, and Vision Insurance
• Free Breakfast, Lunch, and Dinner (where applicable)
• Stocked Micro-Kitchens with Snacks and Beverages
• Commuter Benefits, Including Shuttles and Bike-to-Work Options
• Unique Campus Amenities Depending on Location


Working at Welo Data

What to expect from working at Welo Data

From Welo Data

About Welo Data, in their own words

From Welo Data

Welo Data is a global AI data services company powering the next generation of AI. We build, annotate, and validate the training datasets that make AI models accurate, safe, and ready for the real world — across languages, cultures, and domains.

Our team of experts spans the globe, combining deep technical knowledge with a human-centered approach. If you want your work to shape how AI understands the world, you'll find your place here.

Diversity and inclusion statement

From Welo Data

Our Strength is derived from Winning Together. Welo Data is unequivocally committed to developing and fostering a workplace and organizational culture that values the diversity of thought and perspective delivered by a diverse global workforce operating within an inclusive organization.